A method for unsupervised disaggregation of appliance signatures from smart meter data is presented. The primary feature used for unsupervised learning relates to abrupt transitions or magnitude changes in the power waveform. The method consists of a sequence of procedures for appliance signature identification, and disaggregation using hidden Markov modeling (HMM), and residual analysis. The key contributions are (a) a novel 'segmented' application of the Viterbi algorithm for sequence decoding with the HMM, (b) details of establishing observation and state transition probabilities for the HMM, and (c) procedures for careful handling of low power signatures. Results show that the method is effective for magnitude-based disaggregation, and provide insights for a more complete solution.